Brain computer interfaces (BCIs) function as a direct communication pathway between a human brain and an external device. Furthermore, BCI systems can also provide an important test-bed for the development of mathematical methods and multi-channel signal processing to derive command signals from brain activities. As it directly uses the electrical signatures of the brain's activity for responding to external stimuli, it is particularly useful for paralyzed people who suffer from severe neuromuscular disorders and are hence unable to communicate through the normal neuromuscular pathway. The electroencephalogram (EEG) is one of the widely used techniques out of many existing brain signal measuring techniques due to its advantages such as its non-invasive nature and its low cost.
With regard to rehabilitation, currently, stroke rehabilitation commonly involves physical therapy by human therapists. Robotic rehabilitation may augment human therapists and enable novel rehabilitation exercises which may not be available from human therapists. Clinical trials involving brain-computer interface (BCI) based robotic rehabilitation are currently ongoing and some advantages over standard robotic rehabilitation include robotic assistance to the patient only if motor intent is detected and detection of motor intent being calibrated to patient-specific motor imagery electroencephalogram (EEG).
However, in addition to being at clinical trial level only, it is believed that current techniques are typically limited to rehabilitation of the limbs, i.e. arms and legs of a person.
Furthermore, to use EEG data for rehabilitation purposes, the EEG data is typically modeled into motor control data and idle state data. Typically, the idle state data are not separately modeled but rather, are modeled similarly to control data using a uni-modal approach. The approach is taken because modeling idle state data can be complex and difficult. One problem with the uni-modal approach is that false positive detections signalling control data may be received by a BCI system when a subject is instead in an idle state, e.g. not performing any mental control.
Therefore, there exists a need for a method of calibrating a motor imagery detection module and a system for motor rehabilitation that seek to address at least one of the above problems.